Methods of performing live monitoring of a wireless communication network

ABSTRACT

In a method of performing live monitoring of a wireless communication network, the network may be divided into a plurality of neighborhoods. A neighborhood may be represented by a given cell of interest and one or more neighbor cells of the cell of interest. A desired neighborhood is selected, and one or more given parameters of the selected neighborhood are monitored to evaluate network performance.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to co-pending U.S. patent application Ser.No. 10/325,831 to David Abusch-Magder et al., filed Mar. 7, 2005 andentitled “METHODS OF SIMPLIFYING NETWORK SIMULATION”; and to U.S. patentapplication Ser. No. (Unassigned, Atty. Dkt. No. 29250-002205/US) toDavid Abusch-Magder, filed ______ and entitled “METHOD OF CONFIGURINGCELLS IN A NETWORK USING NEIGHBORHOODS AND METHOD OF DYNAMICALLYCONFIGURING CELLS IN A NETWORK USING NEIGHBORHOODS”. The entire contentsof each of these related applications are hereby incorporated byreference herein.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to methods of performing livemonitoring of a wireless communication network.

2. Description of the Related Art

Traditionally, networks can be monitored in a variety of ways to assessperformance and find problems. Examples of events or parameters in thenetwork that are monitored can include service measurements (hourlycount of various events that happen in a network), per-call servicemeasurements, which involves more detailed and voluminous data that isdelivered on a per call basis, various alarms and conditions on a percell basis, Additionally, network monitoring may include the monitoringof signaling and data channels between a cell and a centralizedcontroller (for example, a mobile switching center (MSC)) or between acell and other network elements (for example, another cell, or anoperations and management server). This allows tracking of much moredetailed data, although the comparative cost is substantial (asubstantial amount of data to monitor, record, and store, and somemethods of monitoring require expensive monitoring equipment which canonly monitor a few cells at a time).

Network or service suppliers/providers may desire occasionally to focuson what's going on at a particular local area, such as in a given cellor several cells, rather than across a broader area of network or acrossthe entire network. Local monitoring can be desired to assess the impactof local changes on the network (local changes such as antennaorientation, power setting, internal parameter, etc), and/or how globalchanges impact a particular trouble/hot spot, or to monitor potentialtrouble spots and/or to observe the impact of a changing trafficpattern. For example, it may be desirable to evaluate a new pattern ofusers (new mall, construction at a highway interchange or a newdistribution of services, i.e., changes in allocation of voice versusdata calls such as evaluating an increase in high-speed data use inbusiness district).

However, conventional network monitoring can be difficult. If thesupplier is monitoring only broad network information, any local effectsmay become obscured; if monitoring cell specific data over a broadnetwork area, there can be a high overhead cost in data collection,processing, resources for collecting data, etc. Moreover, a poorselection of neighboring cells can result in either use of unnecessaryresources (too many cells monitored), lack of pertinent information (toofew cells monitored), or both (indicative of a poor choice of cells tomonitor).

SUMMARY OF THE INVENTION

Exemplary embodiments are directed to a method of performing livemonitoring of a wireless communication network. In an example method,the network may be divided into a plurality of neighborhoods representedby a cell of interest and one or more neighbor cells of the cell ofinterest. A desired neighborhood from the plurality of neighborhoods maybe selected, and one or more given parameters of the selectedneighborhood may be monitored to evaluate network performance.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments of the present invention will become more fullyunderstood from the detailed description given herein below and theaccompanying drawings, wherein like elements are represented by likereference numerals, which are given by way of illustration only and thusare not limitative of the exemplary embodiments of the presentinvention.

FIG. 1 is a flowchart for describing use of a distance measure anddistance-based threshholding for determining a neighborhood to be usedfor live monitoring, in accordance with an exemplary embodiment of thepresent invention.

FIG. 2 is a flowchart for describing reverse link interferencethreshholding for determining a neighborhood to be used for livemonitoring, in accordance with an exemplary embodiment of the presentinvention.

FIG. 3 is a histogram of interference power for a sample cell.

FIG. 4 is a topographic map to illustrate a comparison of neighborhoodsdetermined by reverse link interference with mean threshholdingselection and geographic distance with Top-X selection.

FIG. 5 is a flowchart for describing a method of performing livemonitoring of a wireless communication network with a determinableneighborhood of cells within the network, in accordance with anexemplary embodiment of the present invention.

FIG. 6 illustrates simulated network performance using reverse linkinterference with mean threshold selection to obtain neighborhoods ofdifferent sizes on a realistic over-designed 206-cell network.

FIGS. 7A-7D illustrate correlations in the change in cell coverage(Δcoverage) based on full network and neighborhood simulator after thefirst iteration of an exemplary greedy cell deletion algorithm.

FIG. 8 is a graph illustrating a comparison of the simulated performanceof the greedy cell deletion algorithm for neighborhoods determined byreverse link interference with mean threshholding selection versusneighborhoods determined by geographic distance with Top-X selection.

DETAILED DESCRIPTION OF THE EXEMPLARY EMBODIMENTS

Although the following description relates to a network based on one ormore of CDMA (IS95, cdma2000 and various technology variations), UMTS,GSM, 802.11 and/or related technologies, and will be described in thisexemplary context, it should be noted that the exemplary embodimentsshown and described herein are meant to be illustrative only and notlimiting in any way. As such, various modifications will be apparent tothose skilled in the art for application to communication systems ornetworks based on technologies other than the above, which may be invarious stages of development and intended for future replacement of, oruse with, the above networks or systems.

As used herein, the term mobile may be synonymous to a mobile user, userequipment (UE), user, subscriber, wireless terminal and/or remotestation and may describe a remote user of wireless resources in awireless communication network. The term ‘cell’ may be understood as abase station, access point, and/or any terminus of radio frequencycommunication. Although current network architectures may consider adistinction between mobile/user devices and access points/base stations,the exemplary embodiments described hereafter may generally beapplicable to architectures where that distinction is not so clear, suchas ad hoc and/or mesh network architectures, for example.

The exemplary embodiments of the present invention are directed tomethods of performing live monitoring of a wireless communicationnetwork with a determinable neighborhood of cells within the network. Aswill be described below, and in general, the local nature of wirelessradio propagation may be leveraged in order to reduce the number ofcells and mobiles to be monitored as a representation of networkperformance. One motivation behind this locality property or approachmay be understood as follows: cells located “far enough” apart shouldexert negligible effects on each other. For example, cells on oppositesides of a large metropolitan area should have little effect on eachother.

This locality property may be exploited in an effort to achievesignificant performance gains even within moderately sized markets. Whentrying to assess the impact of changes at a cell of interest, instead ofmonitoring given parameters for the full-network, one can insteadmonitor one or more selected parameters of a much smaller “neighborhood”around the cells of interest that will be representative of the impactthose changes have on the full network.

To provide context for the exemplary embodiments described herein, theinventors provide an overview of a particular network optimizationproblem, generally discuss algorithms that may be used to demonstratelocality simplifications, and provide an introduction to distancemeasures and neighborhood selection criteria.

Cell Site Selection and Cell Deletion

Cell deletion, a variant of the well-known cell site selection problem,is an example of a computationally difficult wireless optimizationproblem whose solution generally requires numerous conventionallarge-scale network simulations. Cell deletion asks how the designershould remove k cells from a network while maximizing a networkperformance measure such as network coverage. This problem is relevantin planning network upgrades, when new technologies often improvenetwork performance so that fewer cells are needed to achieve previousperformance levels. The application of the neighborhood simplificationto this problem exemplifies the utility of the neighborhood concept inanother context, and may help guide the construction of exemplarydistance metrics and selection criteria.

Distance Measures

The effectiveness of locality-based network optimization and monitoringmay be influenced by the definition of “distance” between cells. It isthis notion of distance that permits a neighborhood to be defined aroundeach cell. Generally, a distance measure may be understood as a functionthat “measures” the “distance” between two cells. The distance need notbe a simple measure of the geographic separation, but rather may be ageneralization that should capture how much cells affect each other. Adesirable distance measure would conclude that cells having a relativelylarge effect on each other are closer.

The geographic distance between two cells may be the simplest measure ofdistance. While cells that are geographically farther apart will tend tohave less effect on each other using this distance measure, simplegeographic distance does not incorporate the fundamental interactionbetween cells-radio frequency radiation.

Radio frequency effects should thus be accounted for in order toaccurately measure the distance between cells. The path loss (i.e., ameasure of an attenuation signal between two points) at broadcastfrequencies may serve as a desirable measure of distance to determine agiven neighborhood for a cell of interest, for example. The path lossmay be measured between the cell of interest and one or more points inthe vicinity of another cell, and a score may be determined for othercells based on the path loss of the points in their vicinity in order todetermine cells of the neighborhood.

For example, the path loss may be measured by comparing the powerreceived from another cell by the cell of interest to the powerbroadcast by that other cell. In another example, the path loss may bemeasured by comparing the power received from the cell of interest atmobiles owned by another cell. In a further example, the path loss maybe computed by measuring power from the cell of interest at points inthe vicinity of another cell. The points may or may not be the mobilesowned by the other cell. In another example, a plurality of measurementscan be combined into a single valued measure to be evaluated against thethreshold.

Cells that have greater path loss from the cell of interest would beconsidered “farther”, regardless of the geographic distance betweencells. A neighborhood may then be defined as those cells that have apath loss value below some threshold (which may be set in advance or bebased on another criterion) from the cell of interest, while those cellswith path loss value greater than the threshold would not be included inthe neighborhood. A path loss-based distance measure would thusincorporate the local terrain, the clutter, and the propagationenvironment. Accordingly, once cells having a path loss metric less thanthe threshold are selected as cells of the neighborhood, one or moreselectable parameters of the neighborhood may then be monitored toassess status or performance of the network, as reflected in theneighborhood.

However, given the frequency division duplexing used to separate theforward and reverse links in most cellular systems, it is not theforward link broadcast of one cell that interferes with another cell,but rather the interference on the reverse link. Accordingly, a modifieddistance measure that incorporates the deleterious reverse linkinterference may be desirable when assessing inter-cell interaction. Bymeasuring the power of the reverse link interference at cell of interestA due to the mobiles in communication with another cell B, a distancemeasure between the two cells may thus be defined. This measure, likethe reverse interference, may be dependent on the number of mobilesowned by cell B. By “owned”, the cell (i.e., cell B) that is serving themobile(s) may represent the mobiles' primary radio connection.Alternatively, any mobiles which may be in communication with a givencell (such as cell B in this example), could also represent mobiles thatmay be owned by cell B.

This “distance” measure, like reverse link interference, is notsymmetric; the mobiles served and/or owned by cell A may have a largereffect on cell B than the effect of those mobiles served by cell B haveon cell A. Yet, a distance measure based on a reverse link interferencedefinition may be applicable to a variety of different air interfaces,and may naturally incorporate the differing interactions between cellsinherent in each technology.

Distance Measures & Selection Criteria for Neighborhood Determination

When performing an application such as live monitoring around cell A,one may only need to consider the cells that are close enough to A to berelevant. This may or may not be related to the actual geographicdistance between the cells. For example, a distance measure may be usedto rank the other cells and their importance to the cell of interest.The selection criterion (also referred to as threshholding criterion)determines how many members of that ranked list to include as neighborsof the cell of interest. In the following examples, two differentdistance measures and selection criteria are presented to illustrate theimpact of both the distance measure and selection criterion ondetermining neighborhoods to be used for performing applications such aslive monitoring of network performance.

As to be illustrated in more detail hereafter, cells for a givenneighborhood may be selected using several possible distance measures.In an example, defining a neighborhood including a given cell ofinterest to be selected for performing live monitoring thereon may bedetermined based on geographic distance information or another distancemetric from the cell of interest. Distances measurements may be used andcompared to another distance metric (i.e., selection criterionparameter) to determine a score for each cell. In an example, a scorefor other cells in addition to the given cell of interest may be thusdetermined as a function of the geographic distance of the other cellsfrom the given cell of interest. Based on their scores, given cells maythen be selected for the neighborhood.

As previously discussed, measured path loss data between a given cell ofinterest and one or more points in the vicinity of another cell may alsobe used as the distance measure to determine a score for each cell indetermining the neighborhood. Further, reverse link information of thosemobiles “owned” by a cell being evaluated as a possible neighbor of thecell of interest may be used to determine the neighborhood that isselected for live monitoring.

FIG. 1 is a flowchart 100 for describing use of a distance measure and aselection criterion for determining a neighborhood usable for an examplelive monitoring methodology, in accordance with an exemplary embodimentof the present invention. As shown in FIG. 1, a given cell in thenetwork may be selected (110) for evaluation. In order to determine theneighborhood surrounding the selected cell, a selection criterion may beestablished (125) with the distance measure 120 (be it a geographicmeasure or another measure) to determine the neighbors of the cell ofinterest (140).

Thus, in one example a selection criterion parameter may be embodied asa set number of X cells in the neighborhood of the cell of interest thatare closest; this may be appropriate when using a geographic distancemeasure. Likewise “Top-X” might be used with a different distancemeasure such as a measurement based on path loss or reverseinterference. Accordingly, if a given cell meets the chosen selectioncriteria (output of 130 is ‘YES’), such as the closest X cells(independent of the specific distance measure chosen) it is selected(140) as a cell of the neighborhood, i.e., as a neighbor in theneighborhood to be monitored, and/or additionally to besimulated/optimized for a given application. Those not satisfying theselection criterion (output of 130 is ‘NO’) are rejected (135). Thevalue of X established for Top-X threshold can be varied based on thestructure of the network to be monitored and/or simulated based on theavailability of computational resources, for example.

Accordingly, as discussed above, another method of determining theneighbors of a cell is to select the “top-X” closest cells as neighbors,where X is a pre-selected constant based on the structure of thenetwork. But this “Top-X” methodology is not the only selectiontechnique for neighborhood determination. For example, networkinhomogeneity may make it desirable to have different neighborhood sizesfor different cells. For certain monitoring applications a cell in adense, high-interference area may require more neighbors for accuratemonitoring than a cell in a sparse area, for example.

FIG. 2 is a flowchart 200 for describing reverse link interferencethreshholding for determining a neighborhood usable for an example livemonitoring methodology, in accordance with an exemplary embodiment ofthe present invention. Measuring cells based on reverse linkinterference information and choosing an appropriate selection criterionmay provide a neighborhood that is not restricted to the closestgeographic cells. Such a neighborhood may be a more desirable choice forcertain live network monitoring applications.

An interference distance measure and associated selection criterion mayprovide a more sophisticated methodology for defining neighbors than adistance based approach with “top-X” selection. As shown in FIG. 2, agiven cell in the network may be selected (210) for evaluation. Reverselink interference values may be measured (220) for all mobiles owned byanother cell that can be detected by the given selected cell. Forexample, if cell “A” is the cell to be evaluated, then its neighborhoodwill be determined. All possible other cells, i.e., cell “B”, areconsidered as one of the “other cells” which may or may not be part ofthe neighborhood that is eventually determined to surround cell A. Thereverse link interference at cell A caused by all mobiles incommunication with cell B is determined. As previously discussed,reverse link interference refers to the radio signal(s) measured at cellA created by the mobiles in communication with cell B.

In the “mean reverse interference threshold” selection method eachreverse link interference value may be compared to a reverse linkinterference threshold (230). The threshold is determined as a multipleof the mean value among cells with reverse link interference above theambient noise floor. Cells whose measured reverse link interferencevalues exceed the threshold (output of 230 is ‘YES’) may be selected(240) as cells comprising the neighborhood surrounding the selected cellA to be evaluated. This technique may be referred to as “reverse linkinterference with mean threshholding selection”.

Similarly, distanced based measures may be used to rank cells ascandidates for a neighborhood. Once the cells are ranked (such as byscore) using the elected distance measure, the selection criterion, suchas Top-X, etc, may be used to pare members from the ranked list asmembers to add to the neighborhood to be used for live monitoring, as arepresentative reflection of the neighborhood.

FIG. 3 is a histogram of interference power for a sample cell. Thedistribution of reverse link interference power (shown on a linearscale) is highly right-skewed. In other words, each cell in a network isonly significantly interfered with by a small number of other cells inthe network. This inherent property of wireless networks may enableneighborhoods around cells to be determined by simply selecting cellsbased on a threshold set to be some multiple γ of the mean interferencefor cells whose interference exceed background noise, and defining theneighbors as those cells with interference greater than the threshold.In the histogram, thresholds for γ=0.5, 1, 4 and 10 are marked on thex-axis—a greater γ results in fewer neighbors.

The reverse link interference power distribution for cells is heavilyright-skewed, demonstrating that most of the interference at any cell isdue to a relatively small number of other cells. By choosing anappropriate interference cut-off value and selecting all-cells withinterference scores greater than that value, one may thus obtaininterference-based neighborhoods that are essentially independent of thetotal size of the network.

Accordingly, a selection criterion based on a threshold which is set tosome multiple y of the linear mean of interference power for each cellmay be a desirable alternative to “Top-X” selection with reverseinterference power as a distance metric. Greater values of γ will resultin smaller neighborhoods. A cell-specific mean interference value isused instead of a global mean interference value because cells are oftenin significantly different interference environments.

FIG. 4 is a topographic map to illustrate a comparison of neighborhoodsdetermined by reverse link interference with mean threshholdingselection and geographic distance with Top-X selection techniques. FIG.4 illustrates a section of a realistic network with topography indicatedin grayscale. The cell of interest that is enclosed within a square islabeled A. “Neighboring” cells chosen by both of the techniques (reverselink interference with mean threshholding selection and geographicdistance with Top-X selection) are shown in white (i.e., clear, whitecells).

In FIG. 4, a neighborhood size of 13 is illustrated for reverse linkinterference with mean threshholding selection, and a size of 14 isshown for geographic distance with Top-X selection. Cells chosenexclusively by reverse link interference with mean threshholding areshown as solid elements within a hexagon, and cells chosen exclusivelyby geographic distance with Top-X selection are solid and enclosedwithin a triangle.

FIG. 4 illustrates how reverse link interference with mean threshholdingselection may provide for a more desirable neighborhood choice for thepurposes of evaluating network performance. For example, reverse linkinterference with mean threshholding selection ignores thegeographically-close cell B because it is directed away from the cell ofinterest A. In addition, reverse link interference with meanthreshholding selection selects a distant cell labeled C, which hashigh-traffic and interacts significantly with A.

Live Network Monitoring with Neighborhoods

Network optimization problems distance measures and neighborhoodselection criterion having been briefly discussed, methods of performinglive network monitoring are described hereafter.

FIG. 5 is a flowchart for describing a method of performing livemonitoring of a wireless communication network with a determinableneighborhood of cells within the network, in accordance with anexemplary embodiment of the present invention. As shown in FIG. 5, in anexample method 500, and in general, for a given network that is to bemonitored, the network may be divided (510) into one or moreneighborhoods. Each neighborhood may be determined using a desiredselection criterion as described in FIGS. 1 and 2. A neighborhood may berepresented by a given cell to be evaluated (i.e., cell of interest) andone or more “neighbor” cells of the cell of interest. In someconstructions, a neighborhood could include only the cell of interest,i.e., some cells of interest may be determined as having no neighbors.

From the plurality of determined neighborhoods, a desired neighborhoodmay be selected (520). Various criteria may be employed to select adesired neighborhood from the plurality of selectable neighborhoods,including, but not limited to, via implementation of simulation andoptimization, by evaluation of neighborhood quality metrics as reflectedby determined coefficient values, etc. For the selected neighborhood (at520), which is representative of the entire network, one or more givenparameters thereof may be monitored (530) in an effort to evaluatenetwork performance or determine current status of the network, in thearea surrounding the cell of interest.

For the selected neighborhood, parameters to monitor may in generalinclude service measurements taken of the neighborhood, alarm conditionsin the neighborhood, and/or signaling and data information within theneighborhood. Further examples of parameters that could be monitored asreflective of network status or performance may include, but are notlimited to: per-call measurement data for calls originating, terminatingor passing through the neighborhood; parameter settings for equipment inthe neighborhood (i.e., broadcast power levels, interference levels,signal to noise (Eb/No) ratios, average number of calls, admissioncontrol parameters, etc.); settings in the backhaul network for cells inthe neighborhood (i.e., IP address settings, source and destination ofinter-base station messaging, error rates for backhaul transmission,utilization values for backhaul resources (such as bandwidth), latencyvalues on backhaul); aggregated data for messages passed between cellsof the neighborhood (i.e., handoff requests, handoff successes, anchorswaps, values used for maximal ratio combining, . . . ); results of dataacquired at mobiles and relayed back to cells in the neighborhood; totalintegrated signal strength at the mobile(s), Eb/No value at themobile(s), frame error rate (FER), mobile transmit power, etc. Theseparameters are merely exemplary, one of ordinary skill in the art wouldrecognize other feasible parameters to monitor in the neighborhood thatcould provide a snapshot or reflection of network status and/orperformance.

In another example, the results of the monitoring evaluation may then beused to perform an optimization algorithm for selecting a specificdesired neighborhood to be monitored for further, specific data from aplurality of previously determined, selectable neighborhoods. Moreover,evaluation may be done on a neighborhood-by-neighborhood basis, insteadof on the network as a whole.

An example of a simulation and optimization algorithms may be theiteration of the aforementioned greedy heuristic-based algorithm usingone or more of the neighborhoods to optimize network performance. Asthese exemplary optimization algorithms are described in detail in theco-pending U.S. patent application Ser. No. 10/325,831 to Abusch-Magderet al., a further explanation thereof is omitted for purposes ofbrevity.

FIG. 6 illustrates reverse link interference with mean threshholdingselection on a realistic over-designed 206-cell network. As discussedabove, the parameter γ represents a multiple of the linear mean ofinterference power for each cell. There are six (6) curves shown in FIG.6. Referring from top to bottom (where average neighborhood size on thefirst iteration of the greedy cell deletion algorithm is indicated inbrackets), the six curves are shown as: full network evaluation [all 206cells] (see the “solid circle” curve), γ=0.125[36.7] (vertical linecurve), γ=0.5[20.9] (solid square curve), γ=1[14.7] (hollow triangle),γ=4[8.7] (hollow square) and γ=10 [5.7] (hollow-circle).

In FIG. 6, the γ=0.125 curve closely approximates the full-evaluationcurve even though the γ=0.125 average neighborhood size is almost 6times smaller (206 vs. 36.7 cells). Coverage increases in initialdeletions because the network is over-designed and has too many cells.The resulting interference degrades the coverage. As cells are removedthe interference decreases and coverage increases. After removingapproximately 70 cells, the deletion of cells begins to reduce theoverall coverage.

As FIG. 6 shows, locality-greedy runs produced results of similarquality as compared to conventional-greedy runs. For example, resultscomparable to full-evaluation greedy on a realistic 206-cell networkwere obtained using an average neighborhood size of 36.7. Thissimplification provided an algorithm runtime that was roughly 15 timesfaster than the conventional full-evaluation method, respectively.

FIGS. 7A-7D illustrates correlations between changes in cell coverage(Δcoverage) for a full network evaluation and for a neighborhoodevaluation after the first iteration of the greedy cell deletionalgorithm.

A judicious choice of neighbors surrounding the cell of interest enablescapturing the behavior of the network in the vicinity of the cell ofinterest with fewer resources than would be required using conventionaltechniques, which typically may select a poor choice of neighbors. Tohelp guide this choice and to gain perspective as to the robustness ofthe locality approximation and the utility of neighborhoods, in FIGS.7A-7D the inventor has examined the change in coverage (Δcoverage) afterthe initial deletion of each candidate cell in the same 206-cellrealistic network. The Δcoverage was plotted as computed by a fullevaluation versus Δcoverage as computed by a neighborhood evaluation,and a correlation coefficient between the two was examined.

A high degree of correlations, i.e., a correlation coefficient valuethat is closer to 1, indicates improved accuracy. The neighborhood sizeneeded to obtain results comparable to full-network simulation does notscale proportionately to network size; instead, it remains roughlyconstant. Thus, the benefits possible in monitoring or simulationintroduced by locality may increase with greater network size.

In FIGS. 7A-7D, charts in the same row (7A and 7B, 7C and 7D) correspondto the same neighborhood method but different neighborhood sizes, whilecharts in the same column (7A and 7C, 7B and 7D) have approximately thesame average neighborhood size but use different threshholdingtechniques and distance measures to determine the neighborhood. Averageneighborhood size on the first iteration, indicated in brackets,increases from left to right. The correlation coefficient, and thereforeaccuracy of the locality simplification, increases as neighborhood sizeincreases. For fixed neighborhood size, reverse link interference withmean threshholding selection has a greater correlation than geographicdistance with Top-X selection.

As shown in FIG. 7B, the inventor was able to obtain a near-perfectcorrelation between the two values when using reverse link interferencewith mean threshholding selection with an average neighborhood size of36.7, further underscoring the ability of using the example neighborhoodsimplification techniques to obtain accurate results. Similarmethodologies based on simulation runs could be used to determinedesirable neighborhood selection parameters for network monitoring.Alternatively, a simulation procedure could be carried out to determinerelative utility of neighborhood size/composition based on monitoredresults. Thereafter, those parameters may form the basis for afull-blown network monitoring application.

Choice of Distance Measure and Selection Criterion Affects Quality ofMonitored Data

FIG. 8 is a graph illustrating a comparison of the use of a greedyalgorithm with neighborhoods determined by reverse link interferencewith mean threshholding selection versus neighborhoods determined bygeographic distance with Top-X selection. In FIG. 8, greedy deletionruns on a 206-cell realistic network using different neighborhooddefinitions. Top to bottom (average neighborhood size on first iterationin brackets): full [206], interference mean with γ=1[14.7], geographicdistance with top-15[15]. Even though both neighborhood selectioncriteria have comparable neighborhood sizes (14.7 and 15), the reverselink interference with mean threshholding selection algorithm forneighborhood determination performs substantially better because itselects neighbors in a generally more intelligent manner (as suggestedby FIGS. 7A-7D).

Accordingly, the choice of distance measure or threshholding can have asignificant impact on the performance of the neighborhoodsimplification. In FIG. 8, even though both simulation runs hadapproximately the same average neighborhood size, the reverse linkinterference with mean threshholding selection run showed significantlybetter results.

FIGS. 7A-7D may further illustrate the importance of the choice ofselection criterion for neighborhood determination. In comparing theΔcoverage correlation graphs of similar-sized neighborhoods generatedusing reverse link interference with mean threshholding selection andgeographic distance with Top-X selection, FIGS. 7A-7D clearly showstronger correlations in the reverse link interference with meanthreshholding selection neighborhoods.

The interference-based method performs better because it generatesneighborhoods more intelligently: it is able to includegeographically-distant cells that have high interactions with the cellof interest while ignoring geographically-near cells that do not havemuch interaction. For example, as FIG. 4 shows, cell B is geographicallyclose to A, the cell of interest, but B is also low-traffic and directedaway from A. On the other hand, cell C is relatively far away from B,but C has high-traffic and is omni-directional. Reverse linkinterference with mean threshholding selection includes C but not B,while geographic distance with Top-X selection includes B but not C—theformer is the preferred choice in this situation.

The above example demonstrates how the judicious choice of neighborhoodcan improve the quality of the networking monitoring data gathered forthe same size of neighborhood, or alternatively how to reduce the sizeof the neighborhood and the concomitant costs while maintaining thequality of the network monitoring data gathered. Accordingly, use ofneighborhoods may more accurately capture a network's interaction andtherefore may be desirable for monitoring applications.

Choice of Neighborhood Size Affects Monitoring Accuracy and Efficiency

The choice of a neighborhood should be viewed in the context of acost/benefit analysis for network monitoring. For example, largerneighborhoods cost more to monitor (due to monitoring equipment,computational resources such as storage and processing servers, increasein communication on backhaul etc.), while smaller neighborhoods might beless accurate. Accordingly, a judicious choice of neighborhood isdesired, where the quality of the data gathered may be increased for thesame number of cells, or the quality of the data maintained with areduction in the number of well chosen cells.

Like the trade-off in monitoring, neighborhood size determinationpresents a trade-off in simulations between accuracy and efficiency. Asshown in FIG. 6, simplifications with larger neighborhood sizesgenerally produce more accurate results. However, the inaccuracydecreases as neighborhood size increases because one can obtain resultssimilar to full-evaluation solutions using a relatively smallneighborhood size. The size required for results that agree withfull-network evaluation may depend on the market details, but once theneighborhood is large enough to capture the behavior of the network,increasing neighborhood size simply adds cells and computation whileintroducing little benefit.

Therefore, a judicious choice of neighborhood in simulation may helpguide the choice for network monitoring applications. However, morefundamentally, the same cost-benefit balance exists in choosingneighborhoods for network monitoring: too many cells in the neighborhoodand the monitoring costs are unnecessarily high, too few cells and thequality of the data obtained from monitoring suffers.

Neighborhoods may be used in order to assess neighborhood quality forlive monitoring. For example, dividing networks into neighborhoodsaround cells of interest could be used in assessing the size of aneighborhood required for adequate accuracy in one or more parameters tobe monitored. Such a procedure may be employed to see how robust theselected neighborhood is, and/or to efficiently determine the quality ofthe neighborhood to be monitored for one or more specified parameters.This may also help to conserve processing resources, since one can checka selected neighborhood's robustness characteristics in advance, priorto monitoring the neighborhood for gathering specified information thatreflect performance and/or status of the network.

As an example, a measurement could be made of certain events (forexample call attempts) that coincide with an event in the cell ofinterest (for example a call failure). A parameter such as the number ofsuch coincident events per minute may represent a parameter measure tobe monitored on a local scale, e.g., for a given neighborhood. A similarparameter could be measured on a global scale, e.g. for the entirenetwork or for a larger neighborhood. The number of events in theneighborhood may be the number of failures in the cell of interest thatare coincident with call attempts in the neighborhood (instead of in thenetwork or larger neighborhood as a whole).

To test the quality of the neighborhood to be selected for livemonitoring, a correlation coefficient may be computed based on themeasured parameters for the network version and the neighborhoodversion, in an effort to determine how well the neighborhood capturedsuch events, and hence, may provide a relative indication of the qualityor robustness of the evaluated neighborhood. The neighborhood with thehighest absolute value of its coefficient may be selected as theneighborhood to be used for live monitoring of the chosen parameter(s)for assessing network status or performance. Alternatively, one maymeasure the desirability of a neighborhood based on a combination of afunction of the correlation coefficient and the cost to monitor thatparticular neighborhood. The neighborhood with a desired combined scoremay then be chosen; in this way one can balance the cost and benefit ofdifferent possible neighborhood choices.

Given the trade-off between monitoring costs and accuracy and betweensimulation accuracy and efficiency, one might wonder how, given anetwork, one could select an accurate distance measure and/orneighborhood definition. One method for guiding neighborhood sizedecisions is illustrated by the correlation graphs of FIGS. 7A-7D.Similarly the correlation of first-iteration locality-greedy andconventional-greedy Δcoverage may be a relatively accurate predictor ofoverall locality-greedy performance. Prior to executing a completelocality-greedy run, one could first sample the first-iterationcorrelations with varying neighborhoods to help select appropriateparameters for the full optimization. While this example shows thismethod's utility for network monitoring or for locality-greedy themethod can be generalized, and this correlation method can also beapplied directly to a variety of measurement operations. The limitedsingle-iteration samplings proposed herein can be chosen to requireinsignificant resources relative to a full conventional run. Forexample, the correlation between coincident events as measure globallyand for different neighborhood definitions can serve to guide theappropriate selection of neighborhood definitions. These measurements,when carried out for a limited time or for a limited subset ofcorrelated events, may be a cost effective way of assessing the utilityof a neighborhood definition.

The exemplary embodiments of the present invention being thus described,it will be obvious that the same may be varied in many ways. Suchvariations are not to be regarded as departure from the spirit and scopeof the exemplary embodiments of the present invention, and all suchmodifications as would be obvious to one skilled in the art are intendedto be included within the scope of the following claims.

1. A method of performing live monitoring of a wireless communicationnetwork with a determinable neighborhood of cells within the network,comprising: selecting a given cell in the network to be evaluated,measuring reverse link interference values at the given cell for themobiles owned by or in communication with another cell, wherein theanother cell may or may not be part of the same neighborhood as theselected cell, comparing the reverse link interference values at thegiven cell to a threshold, selecting cells whose reverse linkinterference value exceeds the threshold as cells of the neighborhood ofthe given cell, the neighborhood including the given cell to beevaluated, and monitoring one or more given parameters of theneighborhood to assess the status of the network.
 2. The method of claim1, wherein the given parameters of the neighborhood to monitor includeone or more of service measurements taken of the neighborhood, alarmconditions in the neighborhood and signaling and data information withinthe neighborhood.
 3. The method of claim 1, wherein the measured reverselink measurements are actual measurements of reverse link interferenceat the cell or predicted measurements based on simulation of thenetwork.
 4. The method of claim 1, wherein the threshold is based on amultiple of the linear mean of interference power at the given cell. 5.A method of performing live monitoring of a wireless communicationnetwork with a determinable neighborhood of cells within the network,comprising: selecting a given cell in the network to be evaluated,measuring path loss between the given cell and one or more points in thevicinity of another cell, determining a score for other cells based onthe path loss of the points in their vicinity, selecting cells based ontheir score as cells of a neighborhood, and monitoring one or more givenparameters of the neighborhood to assess the status of the network. 6.The method of claim 5, wherein the given parameters of the neighborhoodto monitor include one or more of service measurements taken of theneighborhood, alarm condition in the neighborhood, signaling and datainformation within the neighborhood,
 7. A method of performing livemonitoring of a wireless communication network with a determinableneighborhood of cells within the network, comprising: selecting a givencell in the network to be evaluated, determining a score for other cellsbased as a function of the geographic distance of the other cells fromthe selected cell, selecting cells based on their score as cells of theneighborhood, and monitoring one or more given parameters of theneighborhood to assess the status of the network.
 8. The method of claim7, wherein the given parameters of the neighborhood to monitor includeone or more of service measurements taken of the neighborhood, alarmcondition in the neighborhood, signaling and data information within theneighborhood,
 9. A method of performing live monitoring of a wirelesscommunication network, comprising: dividing the network into a pluralityof neighborhoods, a neighborhood represented by a given cell to beevaluated and one or more neighbor cells of the given cell, selecting adesired neighborhood, and monitoring one or more given parameters of theselected neighborhood to evaluate network performance.
 10. The method ofclaim 9, wherein the given parameters of the neighborhood to monitorinclude one or more of service measurements taken of the neighborhood,alarm condition in the neighborhood, signaling and data informationwithin the neighborhood.
 11. The method of claim 9, wherein dividingincludes defining a neighborhood including the given cell based onreverse link interference information.
 12. The method of claim 9,wherein dividing includes defining a neighborhood including the givencell based on path loss information between cells.
 13. The method ofclaim 9, wherein dividing includes defining a neighborhood for the givencell based on a geographical distance of other cells from the givencell.
 14. The method of claim 9, wherein selecting further includesevaluating a given number of cells that are closest to the given cellbased on a chosen ranking criterion, the closest X cells selected asbelonging to the neighborhood to be monitored.
 15. The method of claim14, wherein the value of X is variable based on the structure of thenetwork to be simulated.
 16. The method of claim 9, wherein selectingfurther includes selecting the neighborhood as a function of acorrelation coefficient, a value of which is indicative of the qualityof the neighborhood for live network monitoring.
 17. The method of claim16, wherein selecting further includes selecting the neighborhood as afunction of monitoring costs in addition to the correlation coefficient.18. The method of claim 9, wherein selecting further includes:performing one of a simulation and a measurement for each neighborhoodto determine a first parameter for each neighborhood, performing one ofa simulation and a measurement of one of the entire network or a largerneighborhood to determine a second parameter, determining a correlationcoefficient between the first and second parameters, a value of thecorrelation coefficient indicative of the quality of the neighborhoodfor live network monitoring, and selecting the neighborhood with thehighest absolute value of its coefficient as the neighborhood to be usedfor live monitoring to assess network status or performance.
 19. Themethod of claim 18, wherein the first and second parameters are the samemeasured or simulated parameter, the first parameter measured orsimulated on a local, neighborhood scale, the second parameter measuredor simulated on a global network scale or larger neighborhood scale.